Imaging diagnosis and staging of pancreatic ductal adenocarcinoma: a comprehensive review

Hidden in plain sight: commonly missed early signs of pancreatic cancer on CT

Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis mostly due to the advanced stage at which disease is diagnosed. Early detection of disease at a resectable stage is, therefore, critical for improving outcomes of patients. Prior studies have demonstrated that pancreatic abnormalities may be detected on CT in up to 38% of CT studies 5 years before clinical diagnosis of PDAC. In this review, we highlight commonly missed signs of early PDAC on CT. Broadly, these commonly missed signs consist of small isoattenuating PDAC without contour deformity, isolated pancreatic duct dilatation and cutoff, focal pancreatic enhancement and focal parenchymal atrophy, pancreatitis with underlying PDAC, and vascular encasement. Through providing commentary on demonstrative examples of these signs, we demonstrate how to reduce the risk of missing or misinterpreting radiological features of early PDAC.

Unravelling the complexities of resistance mechanism in pancreatic cancer: insights from in vitro and ex-vivo model systems

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor prognosis and rising global deaths. Late diagnosis, due to absent early symptoms and biomarkers, limits treatment mainly to chemotherapy, which soon encounters resistance. PDAC treatment innovation is hampered by its complex and heterogeneous resistant nature, including mutations in key genes and a stromal-rich, immunosuppressive tumour microenvironment. Recent studies on PDAC resistance stress the need for suitable in vitro and ex vivo models to replicate its complex molecular and microenvironmental landscape. This review summarises advances in these models, which can aid in combating chemoresistance and serve as platforms for discovering new therapeutics. Immortalised cell lines offer homogeneity, unlimited proliferation, and reproducibility, but while many gemcitabine-resistant PDAC cell lines exist, fewer models are available for resistance to other drugs. Organoids from PDAC patients show promise in mimicking tumour heterogeneity and chemosensitivity. Bioreactors, co-culture systems and organotypic slices, incorporating stromal and immune cells, are being developed to understand tumour-stroma interactions and the tumour microenvironment's role in drug resistance. Lastly, another innovative approach is three-dimensional bioprinting, which creates tissue-like structures resembling PDAC architecture, allowing for drug screening. These advanced models can guide researchers in selecting optimal in vitro tests, potentially improving therapeutic strategies and patient outcomes.

Mature MUC5AC Expression in Resected Pancreatic Ductal Adenocarcinoma Predicts Treatment Response and Outcomes

Neoadjuvant therapy (NAT) for early-stage pancreatic ductal adenocarcinoma (PDA) has recently gained prominence. We investigated the clinical significance of mucin 5 AC (MUC5AC), which exists in two major glycoforms, a less-glycosylated immature isoform (IM) and a heavily glycosylated mature isoform (MM), as a biomarker in resected PDA. Immunohistochemistry was performed on 100 resected PDAs to evaluate the expression of the IM and MM of MUC5AC using their respective monoclonal antibodies, CLH2 (NBP2-44455) and 45M1 (ab3649). MUC5AC localization (cytoplasmic, apical, and extra-cellular (EC)) was determined, and the H-scores were calculated. Univariate and multivariate (MVA) Cox regression models were used to estimate progression-free survival (PFS) and overall survival (OS). Of 100 resected PDA patients, 43 received NAT, and 57 were treatment-naïve with upfront surgery (UpS). In the study population (n = 100), IM expression (H-scores for objective response vs. no response vs. UpS = 104 vs. 152 vs. 163, p = 0.01) and MM-MUC5AC detection rates (56% vs. 63% vs. 82%, p = 0.02) were significantly different. In the NAT group, MM-MUC5AC-negative patients had significantly better PFS according to the MVA (Hazard Ratio: 0.2, 95% CI: 0.059-0.766, p = 0.01). Similar results were noted in a FOLFIRINOX sub-group (n = 36). We established an association of MUC5AC expression with treatment response and outcomes.

nnU-Net-Based Pancreas Segmentation and Volume Measurement on CT Imaging in Patients with Pancreatic Cancer

RATIONALE AND OBJECTIVES

To develop and validate a deep learning (DL)-based method for pancreas segmentation on CT and automatic measurement of pancreatic volume in pancreatic cancer.

MATERIALS AND METHODS

This retrospective study used 3D nnU-net architecture for fully automated pancreatic segmentation in patients with pancreatic cancer. The study used 851 portal venous phase CT images (499 pancreatic cancer and 352 normal pancreas). This dataset was divided into training (n = 506), internal validation (n = 126), and external test set (n = 219). For the external test set, the pancreas was manually segmented by two abdominal radiologists (R1 and R2) to obtain the ground truth. In addition, the consensus segmentation was obtained using Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. Segmentation performance was assessed using the Dice similarity coefficient (DSC). Next, the pancreatic volumes determined by automatic segmentation were compared to those determined by manual segmentation by two radiologists.

RESULTS

The DL-based model for pancreatic segmentation showed a mean DSC of 0.764 in the internal validation dataset and DSC of 0.807, 0.805, and 0.803 using R1, R2, and STAPLE as references in the external test dataset. The pancreas parenchymal volume measured by automatic and manual segmentations were similar (DL-based model: 65.5 ± 19.3 cm3 and STAPLE: 65.1 ± 21.4 cm3; p = 0.486). The pancreatic parenchymal volume difference between the DL-based model predictions and the manual segmentation by STAPLE was 0.5 cm3, with correlation coefficients of 0.88.

CONCLUSION

The DL-based model efficiently generates automatic segmentation of the pancreas and measures the pancreatic volume in patients with pancreatic cancer.

Improved Pancreatic Cancer Detection and Localization on CT Scans: A Computer-Aided Detection Model Utilizing Secondary Features

The early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for optimal treatment of pancreatic cancer patients. We propose a tumor detection framework to improve the detection of pancreatic head tumors on CT scans. In this retrospective research study, CT images of 99 patients with pancreatic head cancer and 98 control cases from the Catharina Hospital Eindhoven were collected. A multi-stage 3D U-Net-based approach was used for PDAC detection including clinically significant secondary features such as pancreatic duct and common bile duct dilation. The developed algorithm was evaluated using a local test set comprising 59 CT scans. The model was externally validated in 28 pancreatic cancer cases of a publicly available medical decathlon dataset. The tumor detection framework achieved a sensitivity of 0.97 and a specificity of 1.00, with an area under the receiver operating curve (AUROC) of 0.99, in detecting pancreatic head cancer in the local test set. In the external test set, we obtained similar results, with a sensitivity of 1.00. The model provided the tumor location with acceptable accuracy obtaining a DICE Similarity Coefficient (DSC) of 0.37. This study shows that a tumor detection framework utilizing CT scans and secondary signs of pancreatic cancer can detect pancreatic tumors with high accuracy.

The radiomorphological appearance of the invasive margin in pancreatic cancer is associated with tumor budding

PURPOSE

Pancreatic cancer (PDAC) is characterized by infiltrative, spiculated tumor growth into the surrounding non-neoplastic tissue. Clinically, its diagnosis is often established by magnetic resonance imaging (MRI). At the invasive margin, tumor buds can be detected by histology, an established marker associated with poor prognosis in different types of tumors.

METHODS

We analyzed PDAC by determining the degree of tumor spiculation on T2-weighted MRI using a 3-tier grading system. The grade of spiculation was correlated with the density of tumor buds quantified in histological sections of the respective surgical specimen according to the guidelines of the International Tumor Budding Consensus Conference (n = 28 patients).

RESULTS

64% of tumors revealed intermediate to high spiculation on MRI. In over 90% of cases, tumor buds were detected. We observed a significant positive rank correlation between the grade of radiological tumor spiculation and the histopathological number of tumor buds (rs = 0.745, p < 0.001). The number of tumor buds was not significantly associated with tumor stage, presence of lymph node metastases, or histopathological grading (p ≥ 0.352).

CONCLUSION

Our study identifies a readily available radiological marker for non-invasive estimation of tumor budding, as a correlate for infiltrative tumor growth. This finding could help to identify PDAC patients who might benefit from more extensive peripancreatic soft tissue resection during surgery or stratify patients for personalized therapy concepts.

Extracellular volume-based scoring system for tracking tumor progression in pancreatic cancer patients receiving intraoperative radiotherapy

OBJECTIVES

To investigate the value of extracellular volume (ECV) derived from portal-venous phase (PVP) in predicting prognosis in locally advanced pancreatic cancer (LAPC) patients receiving intraoperative radiotherapy (IORT) with initial stable disease (SD) and to construct a risk-scoring system based on ECV and clinical-radiological features.

MATERIALS AND METHODS

One hundred and three patients with LAPC who received IORT demonstrating SD were enrolled and underwent multiphasic contrast-enhanced CT (CECT) before and after IORT. ECV maps were generated from unenhanced and PVP CT images. Clinical and CT imaging features were analyzed. The independent predictors of progression-free survival (PFS) determined by multivariate Cox regression model were used to construct the risk-scoring system. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan-Meier method were used to evaluate the predictive performance of the scoring system.

RESULTS

Multivariable analysis revealed that ECV, rim-enhancement, peripancreatic fat infiltration, and carbohydrate antigen 19-9 (CA19-9) response were significant predictors of PFS (all p < 0.05). Time-dependent ROC of the risk-scoring system showed a satisfactory predictive performance for disease progression with area under the curve (AUC) all above 0.70. High-risk patients (risk score ≥ 2) progress significantly faster than low-risk patients (risk score < 2) (p < 0.001).

CONCLUSION

ECV derived from PVP of conventional CECT was an independent predictor for progression in LAPC patients assessed as SD after IORT. The scoring system integrating ECV, radiological features, and CA19-9 response can be used as a practical tool for stratifying prognosis in these patients, assisting clinicians in developing an appropriate treatment approach.

CRITICAL RELEVANCE STATEMENT

The scoring system integrating ECV fraction, radiological features, and CA19-9 response can track tumor progression in patients with LAPC receiving IORT, aiding clinicians in choosing individual treatment strategies and improving their prognosis.

KEY POINTS

Predicting the progression of LAPC in patients receiving IORT is important. Our ECV-based scoring system can risk stratifying patients with initial SD. Appropriate prognostication can assist clinicians in developing appropriate treatment approaches.

Revolutionizing the Pancreatic Tumor Diagnosis: Emerging Trends in Imaging Technologies: A Systematic Review

Background and Objectives: The pancreas, ensconced within the abdominal cavity, requires a plethora of sophisticated imaging modalities for its comprehensive evaluation, with ultrasonography serving as a primary investigative technique. A myriad of pancreatic pathologies, encompassing pancreatic neoplasia and a spectrum of inflammatory diseases, are detectable through these imaging strategies. Nevertheless, the intricate anatomical confluence and the pancreas's deep-seated topography render the visualization and accurate diagnosis of its pathologies a formidable endeavor. The objective of our paper is to review the best diagnostic imagistic tools for the pancreas. Materials and Methods: we have gathered several articles using Prisma guidelines to determine the best imagistic methods. The imperative of pancreatic scanning transcends its diagnostic utility, proving to be a pivotal element in a multitude of clinical specialties, notably surgical oncology. Within this domain, multidetector computed tomography (MDCT) of the pancreas holds the distinction of being the paramount imaging modality, endorsed for its unrivaled capacity to delineate the staging and progression of pancreatic carcinoma. In synergy with MDCT, there has been a notable advent of avant-garde imaging techniques in recent years. These advanced methodologies, including ultrasonography, endoscopic ultrasonography, contrast-enhanced ultrasonography, and magnetic resonance imaging (MRI) conjoined with magnetic resonance cholangiopancreatography (MRCP), have broadened the horizon of tumor characterization, offering unparalleled depth and precision in oncological assessment. Other emerging diagnostic techniques, such as elastography, also hold a lot of potential and promise for the future of pancreatic imaging. Fine needle aspiration (FNA) is a quick, minimally invasive procedure to evaluate lumps using a thin needle to extract tissue for analysis. It is less invasive than surgical biopsies and usually performed as an outpatient with quick recovery. Its accuracy depends on sample quality, and the risks include minimal bleeding or discomfort. Results, guiding further treatment, are typically available within a week. Elastography is a non-invasive medical imaging technique that maps the elastic properties and stiffness of soft tissue. This method, often used in conjunction with ultrasound or MRI, helps differentiate between hard and soft areas in tissue, providing valuable diagnostic information. It is particularly useful for assessing liver fibrosis, thyroid nodules, breast lumps, and musculoskeletal conditions. The technique is painless and involves applying gentle pressure to the area being examined. The resulting images show tissue stiffness, indicating potential abnormalities. Elastography is advantageous for its ability to detect diseases in early stages and monitor treatment effectiveness. The procedure is quick, safe, and requires no special preparation, with results typically available immediately. Results: The assembled and gathered data shows the efficacy of various techniques in discerning the nature and extent of neoplastic lesions within the pancreas. Conclusions: The most common imaging modalities currently used in diagnosing pancreatic neoplasms are multidetector computed tomography (MDCT), endoscopic ultrasound (EUS), and magnetic resonance imaging (MRI), alongside new technologies, such as elastography.

Targeted Nanoparticle-Based Diagnostic and Treatment Options for Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest cancers, presents significant challenges in diagnosis and treatment due to its aggressive, metastatic nature and lack of early detection methods. A key obstacle in PDAC treatment is the highly complex tumor environment characterized by dense stroma surrounding the tumor, which hinders effective drug delivery. Nanotechnology can offer innovative solutions to these challenges, particularly in creating novel drug delivery systems for existing anticancer drugs for PDAC, such as gemcitabine and paclitaxel. By using customization methods such as incorporating conjugated targeting ligands, tumor-penetrating peptides, and therapeutic nucleic acids, these nanoparticle-based systems enhance drug solubility, extend circulation time, improve tumor targeting, and control drug release, thereby minimizing side effects and toxicity in healthy tissues. Moreover, nanoparticles have also shown potential in precise diagnostic methods for PDAC. This literature review will delve into targeted mechanisms, pathways, and approaches in treating pancreatic cancer. Additional emphasis is placed on the study of nanoparticle-based delivery systems, with a brief mention of those in clinical trials. Overall, the overview illustrates the significant advances in nanomedicine, underscoring its role in transcending the constraints of conventional PDAC therapies and diagnostics.

The high FKBP1A expression in WBCs as a potential screening biomarker for pancreatic cancer

Given the limitation of current routine approaches for pancreatic cancer screening and detection, the mortality rate of pancreatic cancer cases is still critical. The development of blood-based molecular biomarkers for pancreatic cancer screening and early detection which provide less-invasive, high-sensitivity, and cost-effective, is urgently needed. The goal of this study is to identify and validate the potential molecular biomarkers in white blood cells (WBCs) of pancreatic cancer patients. Gene expression profiles of pancreatic cancer patients from NCBI GEO database were analyzed by CU-DREAM. Then, mRNA expression levels of three candidate genes were determined by quantitative RT-PCR in WBCs of pancreatic cancer patients (N = 27) and healthy controls (N = 51). ROC analysis was performed to assess the performance of each candidate gene. A total of 29 upregulated genes were identified and three selected genes were performed gene expression analysis. Our results revealed high mRNA expression levels in WBCs of pancreatic cancer patients in all selected genes, including FKBP1A (p < 0.0001), PLD1 (p < 0.0001), and PSMA4 (p = 0.0002). Among candidate genes, FKBP1A mRNA expression level was remarkably increased in the pancreatic cancer samples and also in the early stage (p < 0.0001). Moreover, FKBP1A showed the greatest performance to discriminate patients with pancreatic cancer from healthy individuals than other genes with the 88.9% sensitivity, 84.3% specificity, and 90.1% accuracy. Our findings demonstrated that the alteration of FKBP1A gene in WBCs serves as a novel valuable biomarker for patients with pancreatic cancer. Detection of FKBP1A mRNA expression level in circulating WBCs, providing high-sensitive, less-invasive, and cost-effective, is simple and feasible for routine clinical setting that can be applied for pancreatic cancer screening and early detection.

Contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) features for characterizing serous microcystic adenomas (SMAs): In comparison to pancreatic neuroendocrine tumors (pNETs)

Objectives

Serous microcystic adenoma (SMA), a primary benign pancreatic tumor which can be clinically followed-up instead of undergoing surgery, are sometimes mis-distinguished as pancreatic neuroendocrine tumor (pNET) in regular preoperative imaging examinations. This study aimed to analyze preoperative contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) features of SMAs in comparison to pNETs.

Material and methods

In this retrospective study, patients with imaging-diagnosed pancreatic lesions were screened between October 2020 to October 2022 (ethical approval No. B2020-309R). Performing by a Siemens Sequoia (Siemens Medical Solutions, Mountain View, CA, USA) equipped with a 5C-1 curved array transducer (3.0-4.5 MHz), CEUS examination was conducted to observe the microvascular perfusion patterns of pancreatic lesions in arterial phase, venous/late phases (VLP) using SonoVue® (Bracco Imaging Spa, Milan, Italy) as the contrast agent. Virtual touch tissue imaging and quantification (VTIQ) - SWE was used to measure the shear wave velocity (SWV, m/s) value to represent the quantitative stiffness of pancreatic lesions. Multivariate logistic regression was performed to analyze potential ultrasound and clinical features in discriminating SMAs and pNETs.

Results

Finally, 30 SMA and 40 pNET patients were included. All pancreatic lesions were pathologically proven via biopsy or surgery. During the arterial phase of CEUS, most SMAs and pNETs showed iso- or hyperenhancement (29/30, 97 % and 31/40, 78 %), with a specific early honeycomb enhancement pattern appeared in 14/30 (47 %) SMA lesions. During the VLP, while most of the SMA lesions remained iso- or hyperenhancement (25/30, 83 %), nearly half of the pNET lesions revealed an attenuated hypoenhancement (17/40, 43 %). The proportion of hypoenhancement pattern during the VLP of CEUS differed significantly between SMAs and pNETs (P = 0.021). The measured SWV value of SMAs was significantly higher than pNETs (2.04 ± 0.70 m/s versus 1.42 ± 0.44 m/s, P = 0.002). Taking a SWV value > 1.83 m/s as a cutoff in differentiating SMAs and pNETs, the area under the receiver operating characteristic curve (AUROC) was 0.825, with sensitivity, specificity and likelihood ratio (+) of 85.71 %, 72.73 % and 3.143, respectively. Multivariate logistic regression revealed that SWV value (m/s) of the pancreatic lesion was an independent variable in discriminating SMA and pNET.

Conclusion

By comprehensively evaluating CEUS patterns and SWE features, SMA and pNET may be well differentiated before the operation. While SMA typically presents as harder lesion in VTIQ-SWE, exhibiting a specific honeycomb hyperenhancement pattern during the arterial phase of CEUS, pNET is characterized by relative softness, occasionally displaying a wash-out pattern during the VLP of CEUS.

Molecular pathology and protein markers for pancreatic cancer: relevance in staging, in adjuvant therapy, in determination of minimal residual disease, and follow-up

The diagnosis and monitoring of disease through the detection of circulating protein biomarkers is a growing field in the practice of oncology. The search for more effective protein biomarkers to aid in the diagnosis and treatment of patients with pancreatic ductal adenocarcinoma (PDAC) remains a valuable area of study, given the aggressive and often occult nature of this malignancy. Liquid biopsies are attractive, as they offer a minimally invasive and cost-effective approach when compared to traditional biopsy methods and imaging modalities used for diagnosis and surveillance. Carbohydrate antigen (CA) 19-9 is currently the most commonly used serum protein biomarker for the diagnosis and monitoring of patients with PDAC, but due to its sensitivity and specificity, its utility remains limited. In this review, we examine how circulating protein biomarkers are used in the diagnosis, prognostication, and surveillance of PDAC. We also highlight protein biomarkers that are currently under investigation that have the potential to enhance our ability to detect early-stage malignancies, predict response to therapy, and monitor for recurrence, but these markers require larger prospective validation studies before they can be widely implemented. Continued efforts to identify and validate novel biomarkers will be crucial for improving the management and outcomes of patients with this challenging disease.

Chemokine Receptor 2 Targeted PET/CT Imaging Distant Metastases in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and treatment-refractory malignancies. The lack of an effective screening tool results in the majority of patients being diagnosed at late stages, which underscores the urgent need to develop more sensitive and specific imaging modalities, particularly in detecting occult metastases, to aid clinical decision-making. The tumor microenvironment of PDAC is heavily infiltrated with myeloid-derived suppressor cells (MDSCs) that express C-C chemokine receptor type 2 (CCR2). These CCR2-expressing MDSCs accumulate at a very early stage of metastasis and greatly outnumber PDAC cells, making CCR2 a promising target for detecting early, small metastatic lesions that have scant PDAC cells. Herein, we evaluated a CCR2 targeting PET tracer (68Ga-DOTA-ECL1i) for PET imaging on PDAC metastasis in two mouse models. Positron emission tomography/computed tomography (PET/CT) imaging of 68Ga-DOTA-ECL1i was performed in a hemisplenic injection metastasis model (KI) and a genetically engineered orthotopic PDAC model (KPC), which were compared with 18F-FDG PET concurrently. Autoradiography, hematoxylin and eosin (H&E), and CCR2 immunohistochemical staining were performed to characterize the metastatic lesions. PET/CT images visualized the PDAC metastases in the liver/lung of KI mice and in the liver of KPC mice. Quantitative uptake analysis revealed increased metastasis uptake during disease progression in both models. In comparison, 18F-FDG PET failed to detect any metastases during the time course studies. H&E staining showed metastases in the liver and lung of KI mice, within which immunostaining clearly demonstrated the overexpression of CCR2 as well as CCR2+ cell infiltration into the normal liver. H&E staining, CCR2 staining, and autoradiography also confirmed the expression of CCR2 and the uptake of 68Ga-DOTA-ECL1i in the metastatic foci in KPC mice. Using our novel CCR2 targeted radiotracer 68Ga-DOTA-ECL1i and PET/CT, we demonstrated the sensitive and specific detection of CCR2 in the early PDAC metastases in two mouse models, indicating its potential in future clinical translation.

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

Pancreas CT assessment for pancreatic ductal adenocarcinoma resectability: effect of tube voltage and slice thickness on image quality and diagnostic performance

OBJECTIVES

To assess the resectability of pancreatic ductal adenocarcinoma (PDAC), the evaluation of tumor vascular contact holds paramount significance. This study aimed to compare the image quality and diagnostic performance of high-resolution (HR) pancreas computed tomography (CT) using an 80 kVp tube voltage and a thin slice (1 mm) for assessing PDAC resectability, in comparison with the standard protocol CT using 120 kVp.

METHODS

This research constitutes a secondary analysis originating from a multicenter prospective study. All participants underwent both the standard protocol pancreas CT using 120 kVp with 3 mm slice thickness (ST) and HR-CT utilizing an 80 kVp tube voltage and 1 mm ST. The contrast-to-noise ratio (CNR) between parenchyma and tumor, along with the degree of enhancement of the abdominal aorta and main portal vein (MPV), were measured and subsequently compared. Additionally, the likelihood of margin-negative resection (R0) was evaluated using a five-point scale. The diagnostic performance of both CT protocols in predicting R0 resection was assessed through the area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 69 patients (37 males and 32 females; median age, 66.5 years) were included in the study. The median CNR of PDAC was 10.4 in HR-CT, which was significantly higher than the 7.1 in the standard CT (P=0.006). Furthermore, HR-CT demonstrated notably higher median attenuation values for both the abdominal aorta (579.5 HU vs. 327.2 HU; P=0.002) and the MPV (263.0 HU vs. 175.6 HU; P=0.004) in comparison with standard CT. Following surgery, R0 resection was achieved in 51 patients. The pooled AUC for HR-CT in predicting R0 resection was 0.727, slightly exceeding the 0.699 of standard CT, albeit lacking a significant statistical distinction (P=0.128).

CONCLUSION

While HR pancreas CT using 80 kVp offered a notably greater degree of contrast enhancement in vessels and a higher CNR for PDAC compared to standard CT, its diagnostic performance in predicting R0 resection remained statistically comparable.

Immuno-PET and Targeted α-Therapy Using Anti-Glypican-1 Antibody Labeled with 89Zr or 211At: A Theranostic Approach for Pancreatic Ductal Adenocarcinoma

Glypican-1 (GPC1) is overexpressed in several solid cancers and is associated with tumor progression, whereas its expression is low in normal tissues. This study aimed to evaluate the potential of an anti-GPC1 monoclonal antibody (GPC1 mAb) labeled with 89Zr or 211At as a theranostic target in pancreatic ductal adenocarcinoma. Methods: GPC1 mAb clone 01a033 was labeled with 89Zr or 211At with a deferoxamine or decaborane linker, respectively. The internalization ability of GPC1 mAb was evaluated by fluorescence conjugation using a confocal microscope. PANC-1 xenograft mice (n = 6) were intravenously administered [89Zr]GPC1 mAb (0.91 ± 0.10 MBq), and PET/CT scanning was performed for 7 d. Uptake specificity was confirmed through a comparative study using GPC1-positive (BxPC-3) and GPC1-negative (BxPC-3 GPC1-knockout) xenografts (each n = 3) and a blocking study. DNA double-strand breaks were evaluated using the γH2AX antibody. The antitumor effect was evaluated by administering [211At]GPC1 mAb (∼100 kBq) to PANC-1 xenograft mice (n = 10). Results: GPC1 mAb clone 01a033 showed increased internalization ratios over time. One day after administration, a high accumulation of [89Zr]GPC1 mAb was observed in the PANC-1 xenograft (SUVmax, 3.85 ± 0.10), which gradually decreased until day 7 (SUVmax, 2.16 ± 0.30). The uptake in the BxPC-3 xenograft was significantly higher than in the BxPC-3 GPC1-knockout xenograft (SUVmax, 4.66 ± 0.40 and 2.36 ± 0.36, respectively; P = 0.05). The uptake was significantly inhibited in the blocking group compared with the nonblocking group (percentage injected dose per gram, 7.3 ± 1.3 and 12.4 ± 3.0, respectively; P = 0.05). DNA double-strand breaks were observed by adding 150 kBq of [211At]GPC1 and were significantly suppressed by the internalization inhibitor (dynasore), suggesting a substantial contribution of the internalization ability to the antitumor effect. Tumor growth suppression was observed in PANC-1 mice after the administration of [211At]GPC1 mAb. Internalization inhibitors (prochlorperazine) significantly inhibited the therapeutic effect of [211At]GPC1 mAb, suggesting an essential role in targeted α-therapy. Conclusion: [89Zr]GPC1 mAb PET showed high tumoral uptake in the early phase after administration, and targeted α-therapy using [211At]GPC1 mAb showed tumor growth suppression. GPC1 is a promising target for future applications for the precise diagnosis of pancreatic ductal adenocarcinoma and GPC1-targeted theranostics.

Automatic Detection of Pancreatic Lesions and Main Pancreatic Duct Dilatation on Portal Venous CT Scans Using Deep Learning

OBJECTIVES

This study proposes and evaluates a deep learning method to detect pancreatic neoplasms and to identify main pancreatic duct (MPD) dilatation on portal venous computed tomography scans.

MATERIALS AND METHODS

A total of 2890 portal venous computed tomography scans from 9 institutions were acquired, among which 2185 had a pancreatic neoplasm and 705 were healthy controls. Each scan was reviewed by one in a group of 9 radiologists. Physicians contoured the pancreas, pancreatic lesions if present, and the MPD if visible. They also assessed tumor type and MPD dilatation. Data were split into a training and independent testing set of 2134 and 756 cases, respectively.A method to detect pancreatic lesions and MPD dilatation was built in 3 steps. First, a segmentation network was trained in a 5-fold cross-validation manner. Second, outputs of this network were postprocessed to extract imaging features: a normalized lesion risk, the predicted lesion diameter, and the MPD diameter in the head, body, and tail of the pancreas. Third, 2 logistic regression models were calibrated to predict lesion presence and MPD dilatation, respectively. Performance was assessed on the independent test cohort using receiver operating characteristic analysis. The method was also evaluated on subgroups defined based on lesion types and characteristics.

RESULTS

The area under the curve of the model detecting lesion presence in a patient was 0.98 (95% confidence interval [CI], 0.97-0.99). A sensitivity of 0.94 (469 of 493; 95% CI, 0.92-0.97) was reported. Similar values were obtained in patients with small (less than 2 cm) and isodense lesions with a sensitivity of 0.94 (115 of 123; 95% CI, 0.87-0.98) and 0.95 (53 of 56, 95% CI, 0.87-1.0), respectively. The model sensitivity was also comparable across lesion types with values of 0.94 (95% CI, 0.91-0.97), 1.0 (95% CI, 0.98-1.0), 0.96 (95% CI, 0.97-1.0) for pancreatic ductal adenocarcinoma, neuroendocrine tumor, and intraductal papillary neoplasm, respectively. Regarding MPD dilatation detection, the model had an area under the curve of 0.97 (95% CI, 0.96-0.98).

CONCLUSIONS

The proposed approach showed high quantitative performance to identify patients with pancreatic neoplasms and to detect MPD dilatation on an independent test cohort. Performance was robust across subgroups of patients with different lesion characteristics and types. Results confirmed the interest to combine a direct lesion detection approach with secondary features such as the MPD diameter, thus indicating a promising avenue for the detection of pancreatic cancer at early stages.

Use of individualized 3D-printed models of pancreatic cancer to improve surgeons' anatomic understanding and surgical planning

OBJECTIVES

Three-dimensional (3D) printing has been increasingly used to create accurate patient-specific 3D-printed models from medical imaging data. We aimed to evaluate the utility of 3D-printed models in the localization and understanding of pancreatic cancer for surgeons before pancreatic surgery.

METHODS

Between March and September 2021, we prospectively enrolled 10 patients with suspected pancreatic cancer who were scheduled for surgery. We created an individualized 3D-printed model from preoperative CT images. Six surgeons (three staff and three residents) evaluated the CT images before and after the presentation of the 3D-printed model using a 7-item questionnaire (understanding of anatomy and pancreatic cancer [Q1-4], preoperative planning [Q5], and education for trainees or patients [Q6-7]) on a 5-point scale. Survey scores on Q1-5 before and after the presentation of the 3D-printed model were compared. Q6-7 assessed the 3D-printed model's effects on education compared to CT. Subgroup analysis was performed between staff and residents.

RESULTS

After the 3D-printed model presentation, survey scores improved in all five questions (before 3.90 vs. after 4.56, p < 0.001), with a mean improvement of 0.57‒0.93. Staff and resident scores improved after a 3D-printed model presentation (p < 0.05), except for Q4 in the resident group. The mean difference was higher among the staff than among the residents (staff: 0.50‒0.97 vs. residents: 0.27‒0.90). The scores of the 3D-printed model for education were high (trainees: 4.47 vs. patients: 4.60) compared to CT.

CONCLUSION

The 3D-printed model of pancreatic cancer improved surgeons' understanding of individual patients' pancreatic cancer and surgical planning.

CLINICAL RELEVANCE STATEMENT

The 3D-printed model of pancreatic cancer can be created using a preoperative CT image, which not only assists surgeons in surgical planning but also serves as a valuable educational resource for patients and students.

KEY POINTS

• A personalized 3D-printed pancreatic cancer model provides more intuitive information than CT, allowing surgeons to better visualize the tumor's location and relationship to neighboring organs. • In particular, the survey score was higher among staff who performed the surgery than among residents. • Individual patient pancreatic cancer models have the potential to be used for personalized patient education as well as resident education.

Modified Appleby procedure for locally advanced pancreatic carcinoma: A primer for the radiologist

Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent pancreatic neoplasm accounting for more than 90% of pancreatic malignancies. Surgical resection with adequate lymphadenectomy remains the only available curative strategy for patients with PDAC. Despite improvements in both chemotherapy regimen and surgical care, body/neck PDAC still conveys a poor prognosis because of the vicinity of major vascular structures, including celiac trunk, which favors insidious disease spread at the time of diagnosis. Body/neck PDAC involving the celiac trunk is considered locally advanced PDAC in most guidelines and therefore not eligible for upfront resection. However, a more aggressive surgical approach (i.e., distal pancreatectomy with splenectomy and en-bloc celiac trunk resection [DP-CAR]) was recently proposed to offer hope for cure in selected patients with locally advanced body/neck PDAC responsive to induction therapy at the cost of higher morbidity. The so-called "modified Appleby procedure" is highly demanding and requires optimal preoperative staging as well as appropriate patient preparation for surgery (i.e., preoperative arterial embolization). Herein, we review current evidence regarding DP-CAR indications and outcomes as well as the critical role of diagnostic and interventional radiology in patient preparation before DP-CAR, and early identification and management of DP-CAR complications.

Witek M, Soper SA. Circulating Tumor Cell Subpopulations Predict Treatment Outcome in Pancreatic Ductal Adenocarcinoma (PDAC) Patients

There is a high clinical unmet need to improve outcomes for pancreatic ductal adenocarcinoma (PDAC) patients, either with the discovery of new therapies or biomarkers that can track response to treatment more efficiently than imaging. We report an innovative approach that will generate renewed interest in using circulating tumor cells (CTCs) to monitor treatment efficacy, which, in this case, used PDAC patients receiving an exploratory new therapy, poly ADP-ribose polymerase inhibitor (PARPi)-niraparib-as a case study. CTCs were enumerated from whole blood using a microfluidic approach that affinity captures epithelial and mesenchymal CTCs using anti-EpCAM and anti-FAPα monoclonal antibodies, respectively. These antibodies were poised on the surface of two separate microfluidic devices to discretely capture each subpopulation for interrogation. The isolated CTCs were enumerated using immunophenotyping to produce a numerical ratio consisting of the number of mesenchymal to epithelial CTCs (denoted "Φ"), which was used as an indicator of response to therapy, as determined using computed tomography (CT). A decreasing value of Φ during treatment was indicative of tumor response to the PARPi and was observed in 88% of the enrolled patients (n = 31). Changes in Φ during longitudinal testing were a better predictor of treatment response than the current standard CA19-9. We were able to differentiate between responders and non-responders using ΔΦ (p = 0.0093) with higher confidence than CA19-9 (p = 0.033). For CA19-9 non-producers, ΔΦ correctly predicted the outcome in 72% of the PDAC patients. Sequencing of the gDNA extracted from affinity-selected CTC subpopulations provided information that could be used for patient enrollment into the clinical trial based on their tumor mutational status in DNA repair genes.

Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis

Lymph node metastases are associated with poor clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). In preoperative imaging, conventional diagnostic modalities do not provide the desired accuracy in diagnosing lymph node metastasis. The current review aims to determine the pooled diagnostic profile of studies examining the role of radiomics features in detecting lymph node metastasis in PDAC. PubMed, Google Scholar, and Embase databases were searched for relevant articles. The quality of the studies was examined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools. Pooled results for sensitivity, specificity, likelihood, and odds ratios with the corresponding 95% confidence intervals (CIs) were calculated using a random-effect model (DerSimonian-Liard method). No significant publication bias was detected among the studies included in this meta-analysis. The pooled sensitivity of the validation datasets included in the study was 77.4% (72.7%, 81.5%) and pooled specificity was 72.4% (63.8, 79.6%). The diagnostic odds ratio of the validation datasets was 9.6 (6.0, 15.2). No statistically significant heterogeneity was detected for sensitivity and odds ratio (P values of 0.3 and 0.08, respectively). However, there was significant heterogeneity concerning specificity (P = 0.003). The pretest probability of having lymph node metastasis in the pooled databases was 52% and a positive post-test probability was 76% after the radiomics features were used, showing a net benefit of 24%. Classifiers trained on radiomics features extracted from preoperative images can improve the sensitivity and specificity of conventional cross-sectional imaging in detecting lymph node metastasis in PDAC.

Yield of Staging Laparoscopy for Pancreatic Cancer in the Modern Era: Analysis of More than 1,000 Consecutive Patients

BACKGROUND

Accurate staging prior to resection of pancreatic ductal adenocarcinoma (PDAC) is imperative to avoid unnecessary operative morbidity and oncologic futility in patients with occult intra-abdominal distant metastases. We aimed to determine the diagnostic yield of staging laparoscopy (SL) and to identify factors associated with increased risk of positive laparoscopy (PL) in the modern era.

STUDY DESIGN

Patients with radiographically localized PDAC who underwent SL from 2017 to 2021 were retrospectively reviewed. The yield of SL was defined as the proportion of patients with PL, including gross metastases and/or positive peritoneal cytology. Factors associated with PL were assessed using univariate analysis and multivariable logistic regression.

RESULTS

Of 1,004 patients who underwent SL, 180 (18%) had PL due to gross metastases (n = 140) and/or positive cytology (n = 96). Patients who had neoadjuvant chemotherapy prior to laparoscopy had lower rates of PL (14% vs 22%, p = 0.002). When the analysis was restricted to chemo-naive patients who had concurrent peritoneal lavage performed, 95 of 419 patients (23%) had PL. In multivariable analysis, PL was associated with younger (<60) age, indeterminate extrapancreatic lesions on preoperative imaging, body/tail tumor location, larger tumor size, and elevated serum CA 19-9 (all p < 0.05). Among patients with no indeterminate extrapancreatic lesions on preoperative imaging, the rate of PL ranged from 1.6% in patients with no risk factors to 42% in young patients with large body/tail tumors and elevated serum CA 19-9.

CONCLUSIONS

The rate of PL in patients with PDAC remains high in the modern era. SL with peritoneal lavage should be considered for the majority of patients prior to resection, specifically those with high-risk features, and ideally prior to neoadjuvant chemotherapy.

Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospective Diagnostic Study

The aim of this study was to develop a novel deep learning (DL) model without requiring large-annotated training datasets for detecting pancreatic cancer (PC) using computed tomography (CT) images. This retrospective diagnostic study was conducted using CT images collected from 2004 and 2019 from 4287 patients diagnosed with PC. We proposed a self-supervised learning algorithm (pseudo-lesion segmentation (PS)) for PC classification, which was trained with and without PS and validated on randomly divided training and validation sets. We further performed cross-racial external validation using open-access CT images from 361 patients. For internal validation, the accuracy and sensitivity for PC classification were 94.3% (92.8-95.4%) and 92.5% (90.0-94.4%), and 95.7% (94.5-96.7%) and 99.3 (98.4-99.7%) for the convolutional neural network (CNN) and transformer-based DL models (both with PS), respectively. Implementing PS on a small-sized training dataset (randomly sampled 10%) increased accuracy by 20.5% and sensitivity by 37.0%. For external validation, the accuracy and sensitivity were 82.5% (78.3-86.1%) and 81.7% (77.3-85.4%) and 87.8% (84.0-90.8%) and 86.5% (82.3-89.8%) for the CNN and transformer-based DL models (both with PS), respectively. PS self-supervised learning can increase DL-based PC classification performance, reliability, and robustness of the model for unseen, and even small, datasets. The proposed DL model is potentially useful for PC diagnosis.

Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions

Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.

Approach to Pancreatic Head Mass in the Background of Chronic Pancreatitis

Chronic pancreatitis (CP) is a known risk factor for pancreatic cancer. CP may present with an inflammatory mass, and differentiation from pancreatic cancer is often difficult. Clinical suspicion of malignancy dictates a need for further evaluation for underlying pancreatic cancer. Imaging modalities remain the mainstay of evaluation for a mass in background CP; however, they have their shortcomings. Endoscopic ultrasound (EUS) has become the go-to investigation. Adjunct modalities such as contrast-harmonic EUS and EUS elastography, as well as EUS-guided sampling using newer-generation needles are useful in differentiating inflammatory from malignant masses in the pancreas. Paraduodenal pancreatitis and autoimmune pancreatitis often masquerade as pancreatic cancer. In this narrative review, we discuss the various modalities used to differentiate inflammatory from malignant masses of the pancreas.

Registration-based biomarkers for neoadjuvant treatment response of pancreatic cancer via longitudinal image registration

Purpose

Pancreatic ductal adenocarcinoma (PDAC) frequently presents as hypo- or iso-dense masses with poor contrast delineation from surrounding parenchyma, which decreases reproducibility of manual dimensional measurements obtained during conventional radiographic assessment of treatment response. Longitudinal registration between pre- and post-treatment images may produce imaging biomarkers that more reliably quantify treatment response across serial imaging.

Approach

Thirty patients who prospectively underwent a neoadjuvant chemotherapy regimen as part of a clinical trial were retrospectively analyzed in this study. Two image registration methods were applied to quantitatively assess longitudinal changes in tumor volume and tumor burden across the neoadjuvant treatment interval. Longitudinal registration errors of the pancreas were characterized, and registration-based treatment response measures were correlated to overall survival (OS) and recurrence-free survival (RFS) outcomes over 5-year follow-up. Corresponding biomarker assessments via manual tumor segmentation, the standardized response evaluation criteria in solid tumors (RECIST), and pathological examination of post-resection tissue samples were analyzed as clinical comparators.

Results

Average target registration errors were 2.56 ± 2.45    mm for a biomechanical image registration algorithm and 4.15 ± 3.63    mm for a diffeomorphic intensity-based algorithm, corresponding to 1-2 times voxel resolution. Cox proportional hazards analysis showed that registration-derived changes in tumor burden were significant predictors of OS and RFS, while none of the alternative comparators, including manual tumor segmentation, RECIST, or pathological variables were associated with consequential hazard ratios. Additional ROC analysis at 1-, 2-, 3-, and 5-year follow-up revealed that registration-derived changes in tumor burden between pre- and post-treatment imaging were better long-term predictors for OS and RFS than the clinical comparators.

Conclusions

Volumetric changes measured by longitudinal deformable image registration may yield imaging biomarkers to discriminate neoadjuvant treatment response in ill-defined tumors characteristic of PDAC. Registration-based biomarkers may help to overcome visual limits of radiographic evaluation to improve clinical outcome prediction and inform treatment selection.

Overcoming the Fibrotic Fortress in Pancreatic Ductal Adenocarcinoma: Challenges and Opportunities

An overabundance of desmoplasia in the tumour microenvironment (TME) is one of the defining features that influences pancreatic ductal adenocarcinoma (PDAC) development, progression, metastasis, and treatment resistance. Desmoplasia is characterised by the recruitment and activation of fibroblasts, heightened extracellular matrix deposition (ECM) and reduced blood supply, as well as increased inflammation through an influx of inflammatory cells and cytokines, creating an intrinsically immunosuppressive TME with low immunogenic potential. Herein, we review the development of PDAC, the drivers that initiate and/or sustain the progression of the disease and the complex and interwoven nature of the cellular and acellular components that come together to make PDAC one of the most aggressive and difficult to treat cancers. We review the challenges in delivering drugs into the fortress of PDAC tumours in concentrations that are therapeutic due to the presence of a highly fibrotic and immunosuppressive TME. Taken together, we present further support for continued/renewed efforts focusing on aspects of the extremely dense and complex TME of PDAC to improve the efficacy of therapy for better patient outcomes.

Evaluation of Perfusion Change According to Pancreatic Cancer and Pancreatic Duct Dilatation Using Free-Breathing Golden-Angle Radial Sparse Parallel (GRASP) Magnetic Resonance Imaging

PURPOSE

To evaluate perfusion changes in the pancreas with pancreatic cancer and pancreatic duct dilatation using dynamic contrast-enhanced MRI (DCE-MRI).

METHOD

We evaluate the pancreas DCE-MRI of 75 patients. The qualitative analysis includes pancreas edge sharpness, motion artifacts, streak artifacts, noise, and overall image quality. The quantitative analysis includes measuring the pancreatic duct diameter and drawing six regions of interest (ROIs) in the three areas of the pancreas (head, body, and tail) and three vessels (aorta, celiac axis, and superior mesenteric artery) to measure the peak-enhancement time, delay time, and peak concentration. We evaluate the differences in three quantitative parameters among the ROIs and between patients with and without pancreatic cancer. The correlations between pancreatic duct diameter and delay time are also analyzed.

RESULTS

The pancreas DCE-MRI demonstrates good image quality, and respiratory motion artifacts show the highest score. The peak-enhancement time does not differ among the three vessels or among the three pancreas areas. The peak-enhancement time and concentrations in the pancreas body and tail and the delay time in the three pancreas areas are significantly longer (p < 0.05) in patients with pancreatic cancer than in those without pancreatic cancer. The delay time was significantly correlated with the pancreatic duct diameters in the head (p < 0.02) and body (p < 0.001).

CONCLUSION

DCE-MRI can display the perfusion change in the pancreas with pancreatic cancer. A perfusion parameter in the pancreas is correlated with the pancreatic duct diameter reflecting a morphological change in the pancreas.

The value of enhanced CT features and texture-signatures in assessing the inflammatory infiltration of pancreatic ductal adenocarcinoma

Purpose

To explore the predictive value of computed tomography (CT) imaging features and CT-based texture analysis in assessing inflammatory infiltration in pancreatic ductal adenocarcinoma (PDAC).

Methods

A total of 43 patients with PDAC confirmed by surgical pathology were included in the study. The clinical, radiological, surgical, and pathological features of the patients were analyzed retrospectively using the chi-square test or Spearman's correlation. Receiver operating characteristic (ROC) curves were utilized to assess the overall predictive ability of the tumor enhancement degree on triphasic contrast-enhanced CT images for the inflammatory infiltration degree in PDAC. Furthermore, all CT data were uploaded to the RadCloud platform for region of interest (ROI) delineation and feature extraction. Then, the Variance Threshold and SelectKBest algorithms were used to find the optimal CT features. Binary logistic regression was employed to analyze the selected features in all three contrast-enhanced CT phases, and regression equations were formulated. ROC analysis was performed to evaluate the predictive effectiveness of each equation.

Results

The analysis revealed a statistically significant correlation between the degree of differentiation and radiological findings such as necrosis and cystic degeneration, vascular invasion, and the presence of ascites (P < 0.05). The enhancement degree of the tumor in both the arterial and venous phases was significantly correlated with the inflammatory infiltration degree (P < 0.05); however, the areas under the ROC curve (AUCs) of arterial and venous enhancement were 0.570 and 0.542, respectively. Regression equations based on the texture features of triphasic contrast-enhanced tumors were formulated, and their AUCs were 0.982, 0.643, and 0.849.

Conclusion

Conventional radiological features are not significantly correlated with the degree of inflammatory infiltration in PDAC. The enhancement degrees in both the arterial phase and venous phase were statistically correlated with the inflammatory infiltration level but had poor predictive value. The texture features of PDAC on contrast-enhanced CT may show a better assessment value, especially in the arterial phase.

Imaging Recommendations for Diagnosis, Staging, and Management of Pancreatic Cancer

Pancreatic cancer is the fourth most prevalent cause of cancer-related death worldwide, with a fatality rate equal to its incidence rate. Pancreatic cancer is a rare malignancy with a global incidence and death ranking of 14th and 7th, respectively. Pancreatic cancer cases are divided into three categories without metastatic disease: resectable, borderline resectable, or locally advanced disease. The category is determined by the tumor's location in the pancreas and whether it is abutting or encasing the adjacent arteries and/or vein/s.

The stage of disease and the location of the primary tumor determine the clinical presentation: the pancreatic head, neck, or uncinate process, the body or tail, or multifocal disease. Imaging plays a crucial role in the diagnosis and follow-up of pancreatic cancers. Various imaging modalities available for pancreatic imaging are ultrasonography (USG), contrast-enhanced computed tomography (CECT), magnetic resonance imaging (MRI), and 18-fluoro-deoxy glucose positron emission tomography (FDG PET).

Even though surgical resection is possible in both resectable and borderline resectable non-metastatic cases, neoadjuvant chemotherapy with or without radiotherapy has become the standard practice for borderline resectable cases as it gives a high yield of R0 resection.

Validation of a convolutional neural network for the automated creation of curved planar reconstruction images along the main pancreatic duct

PURPOSE

To evaluate the accuracy and time-efficiency of newly developed software in automatically creating curved planar reconstruction (CPR) images along the main pancreatic duct (MPD), which was developed based on a 3-dimensional convolutional neural network, and compare them with those of conventional manually generated CPR ones.

MATERIALS AND METHODS

A total of 100 consecutive patients with MPD dilatation (≥ 3 mm) who underwent contrast-enhanced computed tomography between February 2021 and July 2021 were included in the study. Two radiologists independently performed blinded qualitative analysis of automated and manually created CPR images. They rated overall image quality based on a four-point scale and weighted κ analysis was employed to compare between manually created and automated CPR images. A quantitative analysis of the time required to create CPR images and the total length of the MPD measured from CPR images was performed.

RESULTS

The κ value was 0.796, and a good correlation was found between the manually created and automated CPR images. The average time to create automated and manually created CPR images was 61.7 s and 174.6 s, respectively (P < 0.001). The total MPD length of the automated and manually created CPR images was 110.5 and 115.6 mm, respectively (P = 0.059).

CONCLUSION

The automated CPR software significantly reduced reconstruction time without compromising image quality.

CT Radiomics and Whole Genome Sequencing in Patients with Pancreatic Ductal Adenocarcinoma: Predictive Radiogenomics Modeling

We investigate whether computed tomography (CT) derived radiomics may correlate with driver gene mutations in patients with pancreatic ductal adenocarcinoma (PDAC). In this retrospective study, 47 patients (mean age 64 ± 11 years; range: 42-86 years) with PDAC, who were treated surgically and who underwent preoperative CT imaging at our institution were included in the study. Image segmentation and feature extraction was performed semi-automatically with a commonly used open-source software platform. Genomic data from whole genome sequencing (WGS) were collected from our institution's web-based resource. Two statistical models were then built, in order to evaluate the predictive ability of CT-derived radiomics feature for driver gene mutations in PDAC. 30/47 of all tumor samples harbored 2 or more gene mutations. Overall, 81% of tumor samples demonstrated mutations in KRAS, 68% of samples had alterations in TP53, 26% in SMAD4 and 19% in CDKN2A. Extended statistical analysis revealed acceptable predictive ability for KRAS and TP53 (Youden Index 0.56 and 0.67, respectively) and mild to acceptable predictive signal for SMAD4 and CDKN2A (Youden Index 0.5, respectively). Our study establishes acceptable correlation of radiomics features and driver gene mutations in PDAC, indicating an acceptable prognostication of genomic profiles using CT-derived radiomics. A larger and more homogenous cohort may further enhance the predictive ability.

The effectiveness of nab-paclitaxel plus gemcitabine and gemcitabine monotherapy in first-line metastatic pancreatic cancer treatment: A real-world evidence

Pancreatic cancer is one of the most lethal malignancies with a rise in mortality rates. FOLFIRINOX and nab-paclitaxel plus gemcitabine demonstrated a survival benefit compared to gemcitabine alone. Both protocols are now considered the standard of first-line treatment with no significant difference between them, primarily based on observational studies. Although new therapeutic options have emerged recently, the prognosis remains poor. We conducted a retrospective single-center study on 139 patients treated for metastatic pancreatic adenocarcinoma (mPDAC) with gemcitabine monotherapy (Gem) or nab-paclitaxel + gemcitabine (Nab-P/Gem) in the first line. The aim of our study was to evaluate the effectiveness in terms of overall survival (OS) and progression-free survival (PFS) as well as the influence of patient and disease characteristics on outcomes. Nab-P/Gem resulted in OS of 13.87 months compared to 8.5 months in patients receiving Gem. The same trend was achieved in PFS, 5.37 versus 2.80 months, respectively, but without reaching statistical significance. Furthermore, the 6-month survival in the Nab-P/Gem group was also higher, 78.1% versus 47.8%. In terms of survival, the group of elderly patients, patients of poorer performance, with higher metastatic burden and liver involvement, benefited the most from combination therapy. In our analysis ECOG performance status (p.s.), previous primary tumor surgery, and liver involvement were found to be independent prognostic factors. The addition of nab-paclitaxel to gemcitabine resulted in a significant improvement in the OS of patients with mPDAC. Subgroup analysis demonstrated that patients with some unfavorable prognostic factors benefited the most.

Imaging features of intraductal tubulopapillary neoplasm of the pancreas and its differentiation from conventional pancreatic ductal adenocarcinoma

Intraductal tubulopapillary neoplasms (ITPN) are rare pancreatic tumors (< 1% of exocrine neoplasms) and are considered to have better prognosis than classical pancreatic ductal adenocarcinoma (PDAC). The present study aimed to evaluate imaging features of ITPN in computed tomography (CT) and magnetic resonance (MR) imaging. We performed monocentric retrospective analysis of 14 patients with histopathologically verified ITPN, operated in 2003–2018. Images were available for 12 patients and were analysed independently by two radiologists, blinded to reports. Imaging features were compared to a matched control group consisting of 43 patients with PDAC, matched for sex and age. Histopathologic analysis showed invasive carcinoma component in all ITPN patients. CT-attenuation values of ITPN were higher in arterial and venous phases (62.3 ± 14.6 HU and 68 ± 15.6 HU) than in unenhanced phase (39.2 ± 7.9 HU), compatible with solid lesion enhancement. Compared to PDAC, ITPN lesions had significantly higher HU-values in both arterial and venous phases (arterial and venous phases, p < 0.001). ITPN were significantly larger than PDAC (4.1 ± 2.0 cm versus 2.6 ± 0.84 cm, p = 0.021). ITPN lesions were more often well-circumscribed (p < 0.002). Employing a multiple logistic regression analysis with forward stepwise method, higher HU density in the arterial phase (p = 0.012) and well-circumscribed lesion margins (p = 0.047) were found to be significant predictors of ITPN versus PDAC. Our study identified key imaging features for differentiation of ITPN and PDAC. Isodensity or moderate hypodensity and well-circumscribed margins favor the diagnosis of ITPN over PDAC. Being familiar with CT-features of these rare pancreatic tumors is essential for radiologists to accelerate the diagnosis and narrow the differentials.

Pancreatic Neoplasms: CT Evaluation of the Uncommon Presentations of Common Lesions and Common Presentations of the Uncommon Lesions!

Pancreatic masses are commonly encountered entities in radiology practice. Pancreatic ductal adenocarcinomas (PDAC) are the commonest pancreatic malignancies that typically present as infiltrative hypodense focal masses in the pancreatic head, which are hypoattenuating to the pancreatic parenchyma on pancreatic parenchymal and venous phases. However, there are various atypical imaging features of PDACs that create a diagnostic dilemma like tumor in body or tail, diffuse glandular involvement, isoattenuating tumors, cystic changes, or calcifications. Also, few relatively uncommon pancreatic malignancies like pancreatic neuroendocrine tumors, cystic pancreatic tumors, pancreatic lymphoma, and pancreatic metastases present with overlapping features. Accurate radiological characterization of pancreatic masses is important for optimal management and prognostication. Thus, it is imperative for radiologists to be aware of all the uncommon presentations of common pancreatic lesions and common presentations of uncommon pancreatic lesions to avoid erroneous interpretations and establishing the correct diagnosis.

Pancreatic Incidentaloma

Pancreatic incidentalomas (PIs) represent a clinical entity increasingly recognized due to advances in and easier access to imaging techniques. By definition, PIs should be detected during abdominal imaging performed for indications other than a pancreatic disease. They range from small cysts to invasive cancer. The incidental diagnosis of pancreatic cancer can contribute to early diagnosis and treatment. On the other hand, inadequate management of PIs may result in overtreatment and unneeded morbidity. Therefore, there is a strong need to evaluate the nature and clinical features of individual PIs. In this review, we summarize the major characteristics related to PIs and present suggestions for their management.

Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging

Simple Summary

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.

Abstract

Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.

Is Cell-Free DNA Testing in Pancreatic Ductal Adenocarcinoma Ready for Prime Time?

Cell-free DNA (cfDNA) testing currently does not have a significant role in PDA management: it is insufficient to diagnose PDA, and its use is primarily restricted to identifying targetable mutations (if tissue is insufficient or unavailable). cfDNA testing has the potential to address critical needs in PDA management, such as pre-operative risk stratification (POR), prognostication, and predicting (and monitoring) treatment response. Prior studies have focused primarily on somatic mutations, specifically KRAS variants, and have shown limited success in addressing prognosis and POR. Recent studies have demonstrated the importance of other less prevalent mutations (ERBB2 and TP53), but no studies have provided reliable mutation panels for clinical use. Methylation aberrations in cfDNA (epigenetic markers) in PDA have been relatively less explored. However, early evidence has suggested they offer diagnostic and, to some extent, prognostic value. The inclusion of epigenetic markers of cfDNA adds another dimension to genomic testing and may open new therapeutic avenues beyond addressing critical areas of need in PDA treatment. For cfDNA to substantially influence PDA management, concerted efforts are required to include less frequent mutations and epigenetic markers. Furthermore, relying on KRAS mutations for PDA management will always be inadequate.

Percutaneous ultrasound and endoscopic ultrasound-guided biopsy of solid pancreatic lesions: An analysis of 1074 lesions

BACKGROUNDS

Percutaneous ultrasound (US) and endoscopic ultrasound (EUS)-guided pancreatic biopsies are widely accepted in the diagnosis of pancreatic diseases. Studies comparing the diagnostic performance of US- and EUS-guided pancreatic biopsies are lacking. This study aimed to evaluate and compare the diagnostic yields of US- and EUS-guided pancreatic biopsies and identify the risk factors for inconclusive biopsies.

METHODS

Of the 1074 solid pancreatic lesions diagnosed from January 2017 to February 2021 in our center, 275 underwent EUS-guided fine needle aspiration (EUS-FNA), and 799 underwent US-guided core needle biopsy (US-CNB/FNA). The outcomes were inconclusive pathological biopsy, diagnostic accuracy and the need for repeat biopsy. All of the included factors and diagnostic performances of both US-CNB/FNA and EUS-FNA were compared, and the independent predictors for the study outcomes were identified.

RESULTS

The diagnostic accuracy was 89.8% for EUS-FNA and 95.2% for US-CNB/FNA (P = 0.001). Biopsy under EUS guidance [odds ratio (OR) = 1.808, 95% confidence interval (CI): 1.083-3.019; P = 0.024], lesion size < 2 cm (OR = 2.069, 95% CI: 1.145-3.737; P = 0.016), hypoechoic appearance (OR = 0.274, 95% CI: 0.097-0.775; P = 0.015) and non-pancreatic ductal adenocarcinoma carcinoma (PDAC) diagnosis (OR = 2.637, 95% CI: 1.563-4.449; P < 0.001) were identified as factors associated with inconclusive pathological biopsy. Hypoechoic appearance (OR = 0.236, 95% CI: 0.064-0.869; P = 0.030), lesions in the uncinate process of the pancreas (OR = 3.506, 95% CI: 1.831-6.713; P < 0.001) and non-PDAC diagnosis (OR = 2.622, 95% CI: 1.278-5.377; P = 0.009) were independent predictors for repeat biopsy. Biopsy under EUS guidance (OR = 2.024, 95% CI: 1.195-3.429; P = 0.009), lesions in the uncinate process of the pancreas (OR = 1.776, 95% CI: 1.014-3.108; P = 0.044) and hypoechoic appearance (OR = 0.127, 95% CI: 0.047-0.347; P < 0.001) were associated with diagnostic accuracy.

CONCLUSIONS

In conclusion, both percutaneous US- and EUS-guided biopsies of solid pancreatic lesions are safe and effective; though the diagnostic accuracy of EUS-FNA is inferior to US-CNB/FNA. A tailored pancreatic biopsy should be considered a part of the management algorithm for the diagnosis of solid pancreatic disease.

Pancreatic Adenocarcinoma: Diagnostic Errors, Contributing Factors and Solutions

The purpose of this article is to review diagnostic errors in preoperative and post-operative imaging for pancreatic ductal adenocarcinoma (PDAC), discuss contributing factors, and provide solutions that minimize errors. Accurate radiological staging and restaging of PDAC dictates surgical management and errors can have significant negative effects on patient care, such as missed vessel involvement or metastatic disease that would preclude surgery. Familiarity with these errors and their contributing factors improves diagnostic accuracy and ultimately leads to improved patient outcomes.

Exosomes as prognostic biomarkers in pancreatic ductal adenocarcinoma-a systematic review and meta-analysis

Extensive research is focused on the role of liquid biopsy in pancreatic cancer since reliable diagnostic and follow-up biomarkers represent an unmet need for this highly lethal malignancy. We performed a systematic review and meta-analysis on the prognostic value of exosomal biomarkers in pancreatic ductal adenocarcinoma (PDAC). MEDLINE, Embase, Scopus, Web of Science, and CENTRAL were systematically searched on the 18th of January, 2021 for studies reporting on the differences in overall (OS) and progression-free survival (PFS) in PDAC patients with positive vs negative exosomal biomarkers isolated from blood. The random-effects model estimated pooled multivariate-adjusted (AHR) and univariate hazard ratios (UHRs) with 95% confidence intervals (CIs). Eleven studies comprising 634 patients were eligible for meta-analysis. Detection of positive exosomal biomarkers indicated increased risk of mortality (UHR = 2.81, CI:1.31-6,00, I2 = 88.7%, P < 0.001), and progression (UHR = 3.33, CI: 2.33-4.77, I2 = 0, P = 0.879) across various disease stages. Positive exosomal biomarkers identified preoperatively revealed a higher risk of mortality in resectable stages (UHR = 5.55, CI: 3.24-9.49, I2 = 0, P = 0.898). The risk of mortality in unresectable stages was not significantly increased with positive exosomal biomarkers (UHR = 2.51, CI: 0.55-11.43, I2 = 90.3%, P < 0.001). Detectable exosomal micro ribonucleic acids were associated with a decreased OS (UHR = 4.08, CI: 2.16-7.69, I2 = 46.9%, P = 0.152) across various stages. Our results reflect the potential of exosomal biomarkers for prognosis evaluation in PDAC. The associated heterogeneity reflects the variability of study methods and need for their uniformization before transition to clinical use.

Clinical features and prognostic impact of asymptomatic pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is highly lethal, and early diagnosis is challenging. Because patients who present with symptoms generally have advanced-stage diseases, analysis of asymptomatic PDAC provides invaluable information for developing strategies for early diagnosis. Here, we reviewed 577 patients with PDAC (372 diagnosed with symptoms [symptomatic group] and 205 without symptoms [asymptomatic group]) diagnosed at our institute. Among the 205 asymptomatic PDAC patients, 109 were detected during follow-up/work-up for other diseases, 61 because of new-onset or exacerbation of diabetes mellitus, and 35 in a medical check-up. Asymptomatic PDAC is characterized by smaller tumor size, earlier disease stage, and higher resectability than those of symptomatic PDAC. In 22.7% of asymptomatic cases, indirect findings, e.g., dilatation of the main pancreatic duct, triggered PDAC detection. Although pancreatic tumors were less frequently detected, overall abnormality detection rates on imaging studies were nearly 100% in asymptomatic PDAC. Asymptomatic PDAC had a better prognosis (median survival time, 881 days) than symptomatic PDAC (342 days, P < 0.001). In conclusion, diagnosis of PDAC in the asymptomatic stage is associated with early diagnosis and a better prognosis. Incidental detection of abnormal findings during the follow-up/work-up for other diseases provides important opportunities for early diagnosis of asymptomatic PDAC.

Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review

Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

Dual-energy CT with virtual monoenergetic images to improve the visualization of pancreatic supplying arteries: the normal anatomy and variations

Background

Pancreatic ductal adenocarcinoma (PDAC) remains a malignancy with poor prognosis, appropriate surgical resection and neoadjuvant therapy depend on the accurate identification of pancreatic supplying arteries. We aim to evaluate the ability of monoenergetic images (MEI [+]) of dual-energy CT (DECT) to improve the visualization of pancreatic supplying arteries compared to conventional polyenergetic images (PEI) and investigate the implications of vascular variation in pancreatic surgery and transarterial interventions.

Results

One hundred patients without pancreatic diseases underwent DECT examinations were retrospectively enrolled in this study. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) at 40-keV MEI (+) were significantly higher than those of PEI (p < 0.05). All subjective MEI (+) scores were significantly higher than those of PEI (p < 0.05). The visualization rates were significantly higher for posterior superior pancreaticoduodenal artery (PSPDA), anterior and posterior inferior pancreaticoduodenal artery (AIPDA, PIPDA), anterior and posterior pancreaticoduodenal arcade (APAC, PPAC), transverse and caudal pancreatic artery (TPA, PCA) at 40-keV MEI (+) than those of PEI (p < 0.05). However, there were no significant differences for visualizing anterior superior pancreaticoduodenal artery (ASPDA), inferior pancreaticoduodenal artery (IPDA), dorsal and magnificent pancreatic artery (DPA, MPA) between 40-keV MEI (+) and PEI (p > 0.05). Four types of variations were observed in the origin of DPA and three to five types in the origin of PSPDA, AIPDA and PIPDA.

Conclusions

40-keV MEI (+) of DECT improves the visualization and objective and subjective image quality of pancreatic supplying arteries compared to PEI. Pancreatic supplying arteries have great variations, which has important implications for preoperative planning of technically challenging surgeries and transarterial interventions.

High-performance Collective Biomarker from Liquid Biopsy for Diagnosis of Pancreatic Cancer Based on Mass Spectrometry and Machine Learning

Background: Most pancreatic cancers are found at progressive stages when they cannot be surgically removed. Therefore, a highly accurate early detection method is urgently needed.

Methods: This study analyzed serum from Japanese patients who suffered from pancreatic ductal adenocarcinoma (PDAC) and aimed to establish a PDAC-diagnostic system with metabolites in serum. Two groups of metabolites, primary metabolites (PM) and phospholipids (PL), were analyzed using liquid chromatography/electrospray ionization mass spectrometry. A support vector machine was employed to establish a machine learning-based diagnostic algorithm.

Results: Integrating PM and PL databases improved cancer diagnostic accuracy and the area under the receiver operating characteristic curve. It was more effective than the algorithm based on either PM or PL database, or single metabolites as a biomarker. Subsequently, 36 statistically significant metabolites were fed into the algorithm as a collective biomarker, which improved results by accomplishing 97.4% and was further validated by additional serum. Interestingly, specific clusters of metabolites from patients with preoperative neoadjuvant chemotherapy (NAC) showed different patterns from those without NAC and were somewhat comparable to those of the control.

Conclusion: We propose an efficient screening system for PDAC with high accuracy by liquid biopsy and potential biomarkers useful for assessing NAC performance.

Prevalence, features, and explanations of missed and misinterpreted pancreatic cancer on imaging: a matched case-control study

PURPOSE

To characterize the prevalence of missed pancreatic masses and pancreatic ductal adenocarcinoma (PDAC)-related findings on CT and MRI between pre-diagnostic patients and healthy individuals.

MATERIALS AND METHODS

Patients diagnosed with PDAC (2010-2016) were retrospectively reviewed for abdominal CT- or MRI-examinations 1 month-3 years prior to their diagnosis, and subsequently matched to controls in a 1:4 ratio. Two blinded radiologists scored each imaging exam on the presence of a pancreatic mass and secondary features of PDAC. Additionally, original radiology reports were graded based on the revised RADPEER criteria.

RESULTS

The cohort of 595 PDAC patients contained 60 patients with a pre-diagnostic CT and 27 with an MRI. A pancreatic mass was suspected in hindsight on CT in 51.7% and 50% of cases and in 1.3% and 0.9% of controls by reviewer 1 (p < .001) and reviewer 2 (p < .001), respectively. On MRI, a mass was suspected in 70.4% and 55.6% of cases and 2.9% and 0% of the controls by reviewer 1 (p < .001) and reviewer 2 (p < .001), respectively. Pancreatic duct dilation, duct interruption, focal atrophy, and features of acute pancreatitis is strongly associated with PDAC (p < .001). In cases, a RADPEER-score of 2 or 3 was assigned to 56.3% of the CT-reports and 71.4% of MRI-reports.

CONCLUSION

Radiological features as pancreatic duct dilation and interruption, and focal atrophy are common first signs of PDAC and are often missed or unrecognized. Further investigation with dedicated pancreas imaging is warranted in patients with PDAC-related radiological findings.

Diagnosing pancreatic neuroendocrine tumors in patients with multiple endocrine neoplasia type 1 in daily practice

BACKGROUND

In multiple endocrine neoplasia type 1 (MEN1), pancreatic neuroendocrine tumors (PanNETs) have a high prevalence and represent the main cause of death. This study aimed to assess the diagnostic accuracy of the currently used conventional pancreatic imaging techniques and the added value of fine needle aspirations (FNAs).

METHODS

Patients who had at least one imaging study were included from the population-based MEN1 database of the DutchMEN Study Group from 1990 to 2017. Magnetic resonance imaging (MRI), computed tomography (CT), endoscopic ultrasonography (EUS), FNA, and surgical resection specimens were obtained. The first MRI, CT, or EUS was considered as the index test. For a comparison of the diagnostic accuracy of MRI versus CT, patients with their index test taken between 2010 and 2017 were included. The reference standard consisted of surgical histopathology or radiological follow-up.

RESULTS

A total of 413 patients (92.8% of the database) underwent 3,477 imaging studies. The number of imaging studies per patient increased, and a preference for MRI was observed in the last decade. Overall diagnostic accuracy was good with a positive (PPV) and negative predictive value (NPV) of 88.9% (95% confidence interval, 76.0-95.6) and 92.8% (89.4-95.1), respectively, for PanNET in the pancreatic head and 92.0% (85.3-96.0) and 85.3% (80.5-89.1), respectively, in the body/tail. For MRI, PPV and NPV for pancreatic head tumors were 100% (76.1-100) and 87.1% (76.3-93.6) and for CT, 60.0% (22.9-88.4) and 70.4% (51.3-84.3), respectively. For body/tail tumors, PPV and NPV were 91.3% (72.0-98.8) and 87.0% (75.3-93.9), respectively, for MRI and 100% (74.9-100) and 77.8% (54.3-91.5), respectively, for CT. Pathology confirmed a PanNET in 106 out of 110 (96.4%) resection specimens. FNA was performed on 34 lesions in 33 patients and was considered PanNET in 24 [all confirmed PanNET by histology (10) or follow-up (14)], normal/cyst/unrepresentative in 6 (all confirmed PanNET by follow-up), and adenocarcinoma in 4 (2 confirmed and 2 PanNET). Three patients, all older than 60 years, had a final diagnosis of pancreatic adenocarcinoma.

CONCLUSION

As the accuracy for diagnosing MEN1-related PanNET of MRI was higher than that of CT, MRI should be the preferred (non-invasive) imaging modality for PanNET screening/surveillance. The high diagnostic accuracy of pancreatic imaging and the sporadic occurrence of pancreatic adenocarcinoma question the need for routine (EUS-guided) FNA.

Current trends in the diagnosis of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a dreaded malignancy with a dismal 5-year survival rate despite maximal efforts on optimizing treatment strategies. Currently, early detection is considered to be the most effective way to improve survival as radical resection is the only potential cure. PDAC is often divided into four categories based on the extent of disease: resectable, borderline resectable, locally advanced, and metastatic. Unfortunately, the majority of patients are diagnosed with locally advanced or metastatic disease, which renders them ineligible for curative resection. This is mainly due to the lack of or vague symptoms while the disease is still localized, although appropriate utilization and prompt availability of adequate diagnostic tools is also critical given the aggressive nature of the disease. A cost-effective biomarker with high specificity and sensitivity allowing early detection of PDAC without the need for advanced or invasive methods is still not available. This leaves the diagnosis dependent on radiodiagnostic methods or endoscopic ultrasound. Here we summarize the latest epidemiological data, risk factors, clinical manifestation, and current diagnostic trends and implications of PDAC focusing on serum biomarkers and imaging modalities. Additionally, up-to-date management and therapeutic algorithms are outlined.